Technical Appendix – Modeling Assumptions for Freight Pathway
Components
Freight pathways evaluated by GNA in the report “Moving California Forward: Zero and LowEmission Goods Movement Pathways” included estimated upstream and downstream emissions of
NOx, PM, and GHGs. These emissions estimates are an aggregation of estimated emissions for each
component in the modeled freight pathway. Table 1 summarizes all of the freight pathway
components and associated emissions estimates that were developed as part of this modeling
exercise. Not all pathway components were included in the main report as the priority freight
pathways did not necessarily use every possible technology combination. All pathway components
are reported here for completeness.
Conceptually, a pathway component is an activity, or group of activities, to which emissions
estimates can be attributed based on available literature and inventory data. Pathway components
vary in their level of granularity. For example, “on-dock rail activity” includes all switcher and
linehaul locomotive emissions associated with idling, train building, and loading that occur on port
property. This pathway component obviously includes emissions contributions from numerous
types of equipment and operations. By contrast, the various “short range drayage truck” pathway
components reflect activity associated with a single type of vehicle performing a relatively limited
set of activities (on-road trucking). However, in both cases the pathway components are based on
emissions inventory data that can reasonably be attributed to the described pathway component
without attempting to subdivide the pathway component further than allowed by the available
data.
This appendix summarizes the major assumptions and data sources used to develop the emissions
estimates for each freight pathway component. These descriptions are meant to accompany the
spreadsheet used to model the emissions and are not intended to be a detailed and exhaustive
reporting of the methodologies employed. The reader is encouraged to reference the spreadsheet
in regard to detailed questions associated with calculations, emissions factors, and other specific
data.
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
Table 1. Emissions estimates for components of freight pathways
ID
1
2
3
4a
4b
5
6a
6b
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Freight Pathway Components
POLA Diesel Short Range Drayage
Truck
POLA NG Short Range Drayage
Truck
OAK Diesel Short Range Drayage
Truck
OAK NG Short Range Drayage
Truck
MY2007 Diesel Short Range
Drayage Truck
MY2010 Diesel Short Range
Truck
MY2010 NG Short Range Truck
Advanced NOx Standard Diesel
Short Range Drayage Truck
Diesel Regional Truck
NG Regional Truck
BEV Short Range Drayage Truck
(CA Avg Grid Mix, 2020)
FCV Short Range Drayage Truck
(80% NG, 20% Renewables)
Catenary Diesel Short Range
Drayage Truck
PHEV Short Range Drayage Truck
(100% electric operation)
POLA/POLB CHE (g/ton)
Rail Yard CHE (g/ton)
On-dock Rail Activity (Tier 3
switch + Tier 2 Line haul) (g/ton)
Tier 4 On-dock Rail Activity
(g/ton)
Tier 2 Railyard Switching (g/ton)
Tier 4 Railyard Switching (g/ton)
Tier 2 Linehaul Rail
Tier 4 Linehaul Rail
Electrified Linehaul Rail
Hybrid Tier 4 Railyard Switching
(g/ton)
Electrified On-dock Rail Activity
(g/ton)
Downstream
Upstream
Total
Emissions
(g/ton-mile)
NOx
PM
GHGs
Emissions
(g/ton-mile)
NOx
PM
GHGs
Emissions
(g/ton-mile)
NOx
PM GHGs
0.735
0.012
154
0.094
0.007
42.9
0.829 0.019
197
0.735
0.012
148
0.064
0.003
56.7
0.799 0.014
205
0.77
0.010
156
0.095
0.007
43.4
0.86
0.017
199
0.77
0.010
150
0.064
0.003
57.2
0.83
0.013
207
0.807
0.012
155
0.094
0.007
42.9
0.900 0.019
198
0.366
0.366
0.011
0.011
150
144
0.091
0.062
0.007
0.003
41.8
55.1
0.457 0.018
0.428 0.014
192
199
0.073
0.42
0.42
0.011
0.007
0.007
150
148
142
0.091
0.090
0.061
0.007
0.007
0.003
41.8
41.2
54.4
0.164 0.018
0.51 0.014
0.48 0.010
192
189
197
0.000
0.000
0.000 0.004
0.001
26
0.004 0.001
25.8
0.000
0.000
0.000 0.074
0.016
96
0.074 0.016
96.3
0.000
0.000
0.000 0.004
0.001
26
0.004 0.001
25.8
0.000
2.46
2.82
0.000
0.051
0.067
0.000 0.004
1,258 0.670
1,258 0.670
0.001
0.050
0.050
26
307
307
0.004 0.001 25.8
3.13 0.102 1,566
3.49 0.117 1,566
9.24
0.256
759
0.404
0.030
185
9.65
0.286
944
2.24
0.760
0.122
0.322
0.076
0.000
0.077
0.017
0.004
0.009
0.003
0.000
759
71.5
71.5
22.4
22.5
0.000
0.404
0.038
0.038
0.012
0.012
0.002
0.030
0.003
0.003
0.001
0.001
0.000
185
17.5
17.5
5.48
5.48
11.1
2.64
0.799
0.160
0.334
0.088
0.002
0.107
0.019
0.007
0.010
0.004
0.000
944
89.0
89.0
27.9
27.9
11.1
0.122
0.004
28.6
0.015
0.001
6.99
0.137 0.005
35.6
0.000
0.000
0.000 0.057
0.007
273
0.057 0.007
273
Technical Appendix – Modeling Assumptions for Freight Pathway Components 2
Zero and Low-Emission Goods Movement Pathways
ID
24
25
26
27
28
29
30
31a
31b
32
33
34
35
Freight Pathway Components
Electrified Railyard Switching
(g/ton)
Electrified Freight Shuttle (CA
Avg Grid Mix, 2020)
On-dock Rail Activity (Tier 4
hybrid switch + Tier 2 Line haul)
(g/ton)
Hybrid Tier 4 Line Haul Rail
NG Tier 4 Railyard Switching
(g/ton)
NG Tier 4 Line Haul Rail
Virtual Container Yards
Short Sea Shipping (Tier 2)
Short Sea Shipping (Tier 4)
Truck on Flatbed Car Linehaul
Truck on Flatbed Car Switching
(g/ton)
Tier 4 Truck on Flatbed Car
Linehaul
Tier 4 Truck on Flatbed Car
Switching (g/ton)
Draft – November 8, 2013
Downstream
Upstream
Total
Emissions
(g/ton-mile)
NOx
PM
GHGs
Emissions
(g/ton-mile)
NOx
PM
GHGs
Emissions
(g/ton-mile)
NOx
PM GHGs
0.000
0.000
0.000 0.003
0.000
14.1
0.003 0.000
14.1
0.000
0.000
0.000 0.002
0.000
11.1
0.002 0.000
11.1
2.24
0.077
2.53
0.099
687
0.122
0.076
77.7
24.4
0.167
0.034
0.683
0.004
54.5 0.037 0.001
23.2 0.159 0.005
0.003
17.1 0.011 0.0003 7.27 0.088 0.003
Fixed 5% reduction in VMT for drayage trucks
0.003
16.4 0.009 0.001
4.07 0.176 0.003
0.0005 16.4 0.009 0.001
4.07 0.043 0.001
0.020
47.7 0.025 0.002
11.6 0.709 0.022
1.62
0.035
152
0.081
0.006
37.1
1.70
0.041
189
0.161
0.006
47.7
0.025
0.002
11.6
0.187 0.008
59.3
0.259
0.008
152
0.081
0.006
37.1
0.340 0.014
189
552
0.294 0.022
135
Not considered
Common Assumptions
Cargo Weight Most emissions inventories provided emissions on an annual basis or a per mile
basis, without reference to cargo weights. To produce emissions estimates on a grams/ton or
grams/ton-mile basis, it is necessary to estimate the weight of cargo transported in terms
comparable with the emissions inventory data. For example, emissions provided for a railyard
facility are typically given in tons per year of pollutant. To estimate the grams of pollutant per ton
of cargo, it is necessary to divide the annual emissions by the total tons of cargo handled at the
facility in one year. In some cases, cargo tonnage data is available. More commonly, data for the
number of containers, TEUs, or lifts is available. Hence, it is necessary to assume an average
container weight to ultimately produce an emissions estimate of grams/ton or grams/ton-mile of
cargo. This modeling exercise assumed an average container weight of 10.6 tons, based on the
reported average container weight for the Port of Los Angeles and Port of Long Beach. Fully loaded
containers can be significantly heavier, with maximum rated capacities of up to 32.5 tons, however,
many containers are transported lightly loaded depending on the cargo and many containers in
transit are empties being returned to the ports for export. It is also important to note that this
“average container” represents a forty foot container, not a TEU.
Technical Appendix – Modeling Assumptions for Freight Pathway Components 3
20.5
20.5
59.3
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
Upstream Emissions Argonne National Labs GREET 2012 model is used to estimate upstream
emissions for all freight components. To calculate upstream emissions, fuel consumption rates are
calculated and converted to an mmBTU basis, using the GREET model assumptions for the energy
content of various fuels. Emissions associated with “Feedstock” and “Fuel”, as reported by GREET,
are then summed and reported for NOx, PM, and GHGs. Diesel fuel is estimated to have a 10%
lower carbon intensity than 2010 levels due to the implementation of the LCFS. Natural gas carbon
intensity is not assumed to change. Downstream emissions estimates from GREET are not used as
they do not represent the vehicles or equipment used in the various pathway components. All
upstream emissions assume a scenario year of 2020 and that the California electrical grid mix is the
primary source of energy for stationary and transportation sector electricity consumption.
Upstream emissions from hydrogen-fueled and electric vehicles use emission factors from
California Air Resources Board’s VISION model.
ID #1-6: Short Range Drayage Trucks
Primary data sources
Emissions data - California Air Resources Board EMFAC 2011
(http://www.arb.ca.gov/msei/modeling.htm#emfac2011_web_based_data)
Basic Approach
EMFAC 2011 provides fleet emissions and activity for port trucks on a tons of pollutant/day and
miles/day basis. These two metrics are used to convert emissions and fuel consumption to a grams
of pollutant/mile and gallons/mile basis. Assuming a truck is transporting a single container of
average weight (10.6 tons) results in an estimate of pollutant emissions in grams/ton-mile and fuel
consumption in gallons/ton-mile. Upstream emissions are then calculated based on the calculated
fuel consumption.
Emissions from EMFAC are based on aggregated vehicle speed data, annual average emissions
rates, and a scenario year of 2020. Freight Component IDs 1-4a reflect EMFAC’s assumed vehicle
model year distributions for POLA/POLB and Port of Oakland. Freight Component IDs 4b, 5 and 6a
are intended to reflect emissions from 2007 or 2010 compliant trucks and are limited to model year
2007 or 2010 trucks only. ID 6b is intended to reflect emissions from a truck meeting a future NOx
standard that is 80% below the 2010 standard.
ID #7-8: Regional Drayage Trucks
Primary data sources
Emissions data - California Air Resources Board EMFAC 2011
(http://www.arb.ca.gov/msei/modeling.htm#emfac2011_web_based_data)
Basic Approach
EMFAC 2011 provides fleet emissions and activity for Class 8 in-state trucks (category T7 in
EMFAC) on a tons of pollutant/day and miles/day basis. These two metrics are used to convert
emissions and fuel consumption to a grams of pollutant/mile and gallons/mile basis. Assuming a
truck is transporting a single container of average weight (10.6 tons) results in an estimate of
Technical Appendix – Modeling Assumptions for Freight Pathway Components 4
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
pollutant emissions in grams/ton-mile and fuel consumption in gallons/ton-mile.
emissions are then calculated based on the calculated fuel consumption.
Upstream
Emissions from EMFAC are based on aggregated vehicle speed data, annual average emissions
rates, model years, and a scenario year of 2020.
ID #9,11, & 12: Electric Short Range Drayage Truck
Primary data sources
Fuel consumption data – Catenary Truck Market Study (GNA, 2012)
Basic Approach
BEVs, PHEVs, and Catenary trucks are assumed to be operating entirely on electricity in near-dock
operations and produce zero downstream emissions. Therefore, all emissions are associated with
upstream production and distribution of the fuel. As in the Common Assumptions portion of this
appendix, upstream emissions are calculated based on the fuel use per ton or ton-mile. Using an
estimated 4 kw-hrs/mile for a battery electric truck and assuming a truck is transporting a single
container of average weight (10.6 tons) results in an estimate of energy consumption in kwhrs/ton-mile. Upstream emissions are then calculated using CARB’s VISION model.
ID #10: Fuel Cell Short Range Drayage Truck
Primary data sources
Fuel consumption data – Catenary Truck Market Study (GNA, 2012)
Basic Approach
FCVs are assumed to produce zero downstream emissions. Therefore, all emissions are associated
with upstream production and distribution of the fuel. As in the Common Assumptions portion of
this appendix, upstream emissions are calculated based on the fuel use per ton or ton-mile. Using
an estimated 4.54 lbs of hydrogen per mile for a battery electric truck and assuming a truck is
transporting a single container of average weight (10.6 tons) results in an estimate of energy
consumption in lbs/ton-mile. Upstream emissions are then calculated as previously described
using CARB’s VISION model, assuming that 80% of hydrogen is produced via steam methane
reformation at the fueling station and 20% is produced from renewable sources.
ID #13: POLA/POLB Cargo Handling Equipment
Primary data sources
Emissions data – Port of Long Beach 2011 emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
ARB Cargo Handling Equipment 2011 emissions model
(http://arb.ca.gov/msei/categories.htm#offroad_motor_vehicles)
Basic Approach
Cargo handling emissions are estimated on a grams/ton of cargo basis and reflect the emissions
associated with moving a ton of cargo through a marine terminal, transitioning the cargo from one
Technical Appendix – Modeling Assumptions for Freight Pathway Components 5
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
transportation mode to another (e.g. ship to truck, ship to rail, truck to ship, etc). The ports of Long
Beach and Los Angeles are the only California ports known to publish detailed annual emissions
inventories that allow the assessment of cargo handling emissions on a grams/ton basis. POLB
reports emissions from CHE directly in grams/100,000 tonnes of cargo. Basic unit conversions
allow reporting emissions in a grams/ton of cargo basis. The emissions inventory also reports CO2
emissions. These emissions are used to estimate fuel consumption rates based on GREET data for
grams of CO2 per MMBTU of diesel fuel combusted. Upstream emissions are then calculated using
GREET based on fuel consumption. Note that all port CHE activity is assumed to be equal to POLB
emissions, regardless of the actual port.
2020 emissions are projected from the 2011 POLB emissions inventory by scaling down emissions
and scaling up cargo throughput based on data from ARB’s Cargo Handling Equipment 2011
emissions model. Total projected emissions and growth factors for calendar years 2020 and 2011
were compared to create scaling ratios. These ratios are then applied to the POLB emissions
inventory to estimate cargo throughput and emissions for 2020.
ID #14: Rail Yard Cargo Handling Equipment
Primary data sources
Emissions data – ARB Cargo Handling Equipment 2011 emissions model
(http://arb.ca.gov/msei/categories.htm#offroad_motor_vehicles)
Rail yard activity data -http://www.arb.ca.gov/railyard/commitments/suppcomceqa070511.pdf
Basic Approach
Cargo handling emissions for rail yards were calculated separately from port CHE and use
emissions data in ARB’s Cargo Handling Equipment emissions model. The model reports emissions
on a calendar year basis. For the current modeling exercise, emissions from 2010 are used as this is
the most current year that rail yard cargo activity is available. Calendar year 2020 emissions are
projected by comparing emissions and activity estimates from the ARB Cargo Handling Equipment
model to produce appropriate scaling factors, as described in ID#13. Estimated 2020 annual
emissions from a rail yard are divided by the estimated 2020 annual cargo throughput for that rail
yard to provide NOx and PM emissions in grams/ton of cargo. Cargo throughput data were only
available for four rail yards listed in ARB’s 2010 Rail Yard MOU. Estimated emissions rates are
averaged across all four rail yards, weighted by cargo throughput, to provide composite emissions
rates for NOx and PM. The ARB model does not provide CO2 or fuel consumption estimates. In lieu
of these data, rail yard CHE is assumed to use the same amount of fuel per ton of cargo as port CHE.
Upstream emissions are then calculated using GREET based on fuel consumption.
ID #15: On-Dock Rail Activity
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Technical Appendix – Modeling Assumptions for Freight Pathway Components 6
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
Basic Approach
On-dock rail emissions were derived from the POLB emissions inventory. This inventory provides
total annual emissions in tons per year. Additionally, estimated annual rail activity and associated
cargo throughput are reported in trains/year and tons of cargo/train. Emissions data are further
segregated into on-port and off-port activity as well as line haul and switcher locomotive sources.
To estimate on-dock rail emissions, the total on-port emissions were divided by the total tons of
rail-borne cargo per year, providing emissions estimates on a grams/ton of cargo basis. Note that
these emissions represent emissions associated with train activity on the marine terminal as well as
emissions associated with rail activity between the marine terminals and the near-dock rail yard
(ICTF). Hence, this pathway component describes emissions for cargo originating at a marine
terminal and ending at the near-dock rail facility five miles from the marine terminal and loaded on
a freight train.
ID #16: Tier 4 On-Dock Rail Activity
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Basic Approach
This pathway component estimates emissions associated with the same on-dock rail activity
described in ID# 15, but assumes that all locomotives transitioned to Tier 4 emissions standards.
Emissions estimates were produced by scaling the emissions identified in ID #15 by the percentage
reductions in emissions required by Tier 4 standards relative to the emissions standards met by the
current equipment. It is assumed that all existing line haul locomotives and off-dock switcher
locomotives meet Tier 2 standards. Existing on-dock switcher locomotives are assumed to meet
Tier 3 standards as this is reflective of the current fleet mix for PHL (the on-dock rail operator).
ID #17: Tier 2 Rail Yard Switching
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Basic Approach
Emissions from rail yard switching locomotive activities were derived from the POLB emissions
inventory. This inventory provides total annual emissions in tons per year. Additionally, estimated
annual rail activity and associated cargo throughput are reported in trains/year and tons of
cargo/train. Emissions data are further segregated into on-port and off-port activity as well as line
haul and switcher locomotive sources. To estimate rail yard switching emissions, the off-port
switcher locomotive emissions were divided by the total tons of rail-borne cargo per year,
providing emissions estimates on a grams/ton of cargo basis. Note that these emissions inventories
assume that the trains consist primarily of double-stacked container cars.
Technical Appendix – Modeling Assumptions for Freight Pathway Components 7
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
ID #18: Tier 4 Rail Yard Switching
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Basic Approach
This pathway component estimates emissions associated with the same on-dock rail activity
described in ID# 17, but assumes that all switcher locomotives transitioned to Tier 4 emissions
standards. Emissions estimates were produced by scaling the emissions identified in ID #17 by the
percentage reductions in emissions required by Tier 4 standards relative to the emissions
standards met by the current equipment. It is assumed that all existing off-dock switcher
locomotives meet Tier 2 standards.
ID #19: Tier 2 Line Haul Rail
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Basic Approach
Emissions from line haul locomotive activities were derived from the POLB emissions inventory.
This inventory provides total annual emissions in tons per year. Additionally, estimated annual rail
activity and associated cargo throughput are reported in trains/year and tons of cargo/train.
Emissions data are further segregated into on-port and off-port activity as well as line haul and
switcher locomotive sources. To estimate line haul rail emissions, the off-port line haul locomotive
emissions were divided by the total ton-miles of rail-borne cargo per year, providing emissions
estimates on a grams/ton-mile of cargo basis. The annual ton-miles of rail-borne cargo was
calculated by multiplying the total tonnage of rail-borne cargo travelling to/from the ports by the
estimated distance the cargo travelled along the Alameda Corridor (21 miles) and between the
Central LA and the Air Basin border (84) miles. These distances and geographic constraints were
used because they reflect the geographic region considered in the emissions inventory. Note that
these emissions inventories assume that the trains consist primarily of double-stacked container
cars.
ID #20: Tier 4 Line Haul Rail
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Basic Approach
This pathway component estimates emissions associated with the same on-dock rail activity
described in ID# 19, but assumes that all line haul locomotives transitioned to Tier 4 emissions
standards. Emissions estimates were produced by scaling the emissions identified in ID #19 by the
percentage reductions in emissions required by Tier 4 standards relative to the emissions
Technical Appendix – Modeling Assumptions for Freight Pathway Components 8
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
standards met by the current equipment. It is assumed that all existing line haul locomotives meet
Tier 2 standards.
ID #21: Electrified Line Haul Rail
Primary data sources
Fuel consumption data – Estimated fuel consumption rates for diesel line haul locomotives in
pathway component #19.
Diesel to electricity conversion assumptions – Cambridge Systematics report, “Analysis of Freight
Rail Electrification in the SCAG Region” (2011)
http://freightworks.migcom.com/docManager/1000000129/Draft%20Freight%20Rail%20Electrif
ication.pdf
Basic Approach
Electric line haul locomotives are assumed to be operating entirely on electricity and produce zero
downstream emissions. Therefore, all emissions are associated with upstream production and
distribution of the fuel. As in the Common Assumptions portion of this appendix, upstream
emissions are calculated based on the fuel use per ton-mile. The energy required at the wheels for
electrified freight rail is assumed to be equivalent to diesel fuel consumption associated with Tier 2
line haul locomotives. Using estimated energy conversion efficiencies for electrified and diesel
locomotives, provided by CamSys, the electrical energy demands from the grid were estimated and
reported in kw-hrs/ton-mile. Upstream emissions are then calculated using CARB’s VISION model.
ID #22: Hybrid Tier 4 Rail Yard Switching
Primary data sources
Baseline fuel consumption data – Estimated fuel consumption rates for diesel switcher locomotives
in pathway component #17.
Fuel reduction benefits from hybrid technology – PHL demonstration report for the Green Goat
hybrid switcher locomotive
(http://www.cleanairactionplan.org/civica/filebank/blobdload.asp?BlobID=2463)
Basic Approach
Off-dock hybrid switcher locomotives are assumed to meet Tier 4 emissions requirements while
achieving a 60% reduction in fuel consumption and CO2e emissions. This assumption, while
possibly high, reflects benefits estimated by PHL from their demonstration of a hybrid switcher
locomotive and manufacturer claims. Direct emissions of NOx and PM were assumed to be equal to
the emissions identified in ID #18. CO2e emissions were assumed to be reduced by 60%. Upstream
emissions are then calculated using GREET.
ID #23: Electrified On-dock Rail Activity
Primary data sources
Baseline fuel consumption data – Estimated fuel consumption rates for diesel locomotives in
pathway components #19 and #22.
Technical Appendix – Modeling Assumptions for Freight Pathway Components 9
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
Diesel to electricity conversion assumptions – Cambridge Systematics report, “Analysis of Freight
Rail Electrification in the SCAG Region” (2011)
http://freightworks.migcom.com/docManager/1000000129/Draft%20Freight%20Rail%20Electrif
ication.pdf
Basic Approach
Electrified on-dock rail activities are assumed to be operating entirely on electricity and produce
zero downstream emissions. Therefore, all emissions are associated with upstream production and
distribution of the fuel. As in the Common Assumptions portion of this appendix, upstream
emissions are calculated based on the fuel use per ton of cargo. The energy required at the wheels
for electrified freight rail is assumed to be equivalent to diesel fuel consumption associated with
diesel locomotives, as given in pathway components #19 and #22 for on-dock activity. The fuel
consumption associated with hybrid switcher locomotives (pathway component #22) was used in
lieu of standard Tier 2 diesel switcher fuel consumption estimates to reflect fuel efficiency gains
that would be anticipated from an electrified locomotive in a high idle application like locomotive
switching. Fuel consumption from line haul locomotives is assumed to be equivalent to Tier 2
diesel locomotives (pathway component #19). Using estimated energy conversion efficiencies for
electrified and diesel locomotives, provided by CamSys, the electrical energy demands from the grid
were estimated and reported in kw-hrs/ton. Upstream emissions are then calculated using CARB’s
VISION model.
ID #24: Electrified Rail Yard Switching
Primary data sources
Baseline fuel consumption data – Estimated fuel consumption rates for hybrid diesel locomotives in
pathway component #22.
Diesel to electricity conversion assumptions – Cambridge Systematics report, “Analysis of Freight
Rail Electrification in the SCAG Region” (2011)
http://freightworks.migcom.com/docManager/1000000129/Draft%20Freight%20Rail%20Electrif
ication.pdf
Basic Approach
Electrified rail yard switching activities are assumed to be operating entirely on electricity and
produce zero downstream emissions. Therefore, all emissions are associated with upstream
production and distribution of the fuel. As in the Common Assumptions portion of this appendix,
upstream emissions are calculated based on the fuel use per ton of cargo. The energy required at
the wheels for electrified freight rail is assumed to be equivalent to diesel fuel consumption
associated with hybrid diesel locomotives, as given in pathway component #22 for off-dock
switching. The fuel consumption associated with hybrid switcher locomotives (pathway
component #22) was used in lieu of standard Tier 2 diesel switcher fuel consumption estimates to
reflect fuel efficiency gains that would be anticipated from an electrified locomotive in a high idle
application like locomotive switching. Using estimated energy conversion efficiencies for electrified
and diesel locomotives, provided by CamSys, the electrical energy demands from the grid were
Technical Appendix – Modeling Assumptions for Freight Pathway Components 10
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
estimated and reported in kw-hrs/ton. Upstream emissions are then calculated using CARB’s
VISION model.
ID #25: Electrified Freight Shuttle
Primary data sources
Baseline fuel consumption data – Estimated fuel consumption rates for hybrid diesel locomotives in
pathway component #21.
Basic Approach
Electrified freight shuttles are assumed to be equivalent in energy consumption to electrified line
haul rail as described in pathway component #21. As both technologies produce zero downstream
emissions and are assumed to have the same energy consumption, the emissions from electrified
freight shuttles are equal to electrified line haul rail.
ID #26: On-Dock Rail Activity with Tier 4 Hybrid Switcher
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Fuel consumption and emissions estimates from pathway component #22 (hybrid switcher
locomotive)
Basic Approach
This pathway component estimates emissions associated with the same on-dock rail activity
described in ID# 15, but assumes that all switcher locomotives transitioned to hybrid locomotives
meeting Tier 4 emissions standards. Emissions estimates were produced by combining the
emissions identified in ID #15 for Tier 2 line haul locomotives with the emissions identified in ID
#22 for hybrid switcher locomotives.
ID #27: Hybrid Tier 4 Line Haul Locomotive
This pathway was not considered as there is no known data for such a technology. Further,
hybridization has the greatest potential benefits when applied to high idle operations such as
switcher operations. Benefits decline quickly as vehicle operations become less transient, as would
be expected in line haul operations.
ID #28 & 29: Natural Gas Tier 4 Line Haul and Switcher Locomotives
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Estimated emissions from pathway components #18 and #20.
Technical Appendix – Modeling Assumptions for Freight Pathway Components 11
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
Basic Approach
Downstream NOx and PM emissions estimates for Tier 4 natural gas switcher and line haul
locomotives are identical to ID #18 and #20 (Tier 4 diesel switcher and line haul locomotives),
respectively. Downstream CO2e emissions are corrected to account for the lower carbon intensity
(in grams CO2/mmBTU of fuel) of natural gas as compared to diesel fuel. Upstream emissions are
calculated in GREET for liquefied natural gas as the vehicle fuel.
ID #30: Virtual Container Yards
Primary data sources
Trip reduction estimates – Rutgers University Study “Investigating the Feasibility of Establishing a
Virtual Container Yard to Optimize Empty Container Movement in the NY-NJ Region” (2007)
(http://www.utrc2.org/sites/default/files/pubs/Investigating-Feasibility-of-Establishing-VirtualContainer-Yard.pdf)
METRANS Study “The Logistics of Empty Cargo Containers in the Southern California Region”
(2003)
(http://www.metrans.org/research/final/01-05_Final.pdf)
Basic Approach
The application of virtual container yards (VCY) and resulting benefits is highly specific to a given
region and only rough approximations of the associated benefits can be made. Further, because
emissions benefits are associated with reductions in trips of unloaded trucks, the benefits of VCYs
are not well characterized on a grams/ton or grams/ton-mile of cargo basis. Two studies were
identified that indicated similar ranges of VMT reduction – approximately 5% VMT reduction
across a region’s drayage fleet. Therefore, VCYs are assumed to provide a fixed 5% VMT reduction
that is equated to a 5% reduction in emissions across a drayage truck fleet.
ID #31a and 31b: Short Sea Shipping
Primary data sources
Fuel-based emissions factors – Texas Transportation Institute study for MARAD “A Modal
Comparison of Domestic Freight Transportation Effects on the General Public 2001-2009" (2012)
(http://www.nationalwaterwaysfoundation.org/study/FinalReportTTI.pdf)
US EPA, “Emissions Factors for Locomotives” (2009)
(http://www.epa.gov/nonroad/locomotv/420f09025.pdf)
Basic Approach
Little data is available on marine emissions factors per ton-mile of cargo transported. One of the
few commonly cited sources for such estimates is a study by the Texas Transportation Institute.
This study used fuel-based factors to estimate the downstream emissions for an inland waterway
towing vessel. Downstream emissions factors for marine vessels are derived from EPA’s
locomotive emissions factors as engines of the sizes considered in the report are both captured
under the same emissions regulations and use essentially the same engines. CO2e emissions are
Technical Appendix – Modeling Assumptions for Freight Pathway Components 12
Zero and Low-Emission Goods Movement Pathways
Draft – November 8, 2013
used to calculate fuel consumption rates based on a reported factor of 98.97 diesel gallons/ton of
CO2e. Upstream emissions are calculated using GREET values for non-road diesel fuel.
ID 31a reflects emissions from a Tier 2 compliant marine engine and ID 31b reflects emissions from
a Tier 4 compliant marine engine.
ID #32-34: Truck on Flatbed Car – Line haul and Switcher Locomotives
Primary data sources
Emissions data – Port of Long Beach emissions inventory
(http://www.polb.com/environment/air/emissions.asp)
Basic Approach
This pathway component estimates emissions associated with rail activity to move semi-tractors
with trailers on flat bed rail cars. Both Tier 2 and Tier 4 emissions estimates follow the same
approach as described in components #17-#20. Specifically, Tier 2 emissions are derived from
annual POLB emissions inventory data. Tier 4 emissions estimates are produced by scaling the Tier
2 emissions estimates by the percentage reductions in emissions required by Tier 4 standards
relative to the Tier 2 standards.
The emissions estimates for truck on flatbed railcars differ from the trains of double-stacked
container cars reported in the POLB inventory. Trucks on flatbed railcars cannot be double stacked
and have significant additional weight associated with the truck and trailer chassis that contribute
to the gross train weight and limit the amount of cargo being transported by a single train. To
account for these weight impacts, the weight of the truck and chassis are estimated at 24,000 lbs
and one rail car per truck is required. Using the same gross train weight reported in the POLB
emissions inventory, the tonnage of cargo per train is calculated. The per-train cargo throughput is
multiplied by the number of trains per year to produce an annual cargo throughput estimate and
used to report emissions on a grams/ton or grams/ton-mile basis. Fuel consumption is estimated
based on CO2e emissions. Upstream emissions are then calculated using GREET and based on low
sulfur diesel fuel.
Technical Appendix – Modeling Assumptions for Freight Pathway Components 13